Moo-Ryong Ra, Bin Liu, Tom La Porta, Ramesh Govindan
The ubiquity of smartphones and their on-board sensing capabilities motivates crowd-sensing, a capability that harnesses the power of crowds to collect sensor data from a large number of mobile
phone users. Unlike previous work on wireless sensing, crowd-sensing poses several novel requirements: support for humans-in-the-loop to trigger sensing actions or review results, the need for incentives, as well as privacy and security. Beyond existing crowd-sourcing systems, crowd-sensing exploits sensing and processing capabilities of mobile devices. In this paper, we design and implement Medusa, a novel programming framework for crowd-sensing that satisfies these requirements. Medusa provides high-level abstractions for specifying the steps required to complete a crowd-sensing task, and employs a distributed runtime system that coordinates the execution of these tasks between smartphones and a cluster on the cloud. We have implemented ten crowd-sensing tasks on a prototype of Medusa. We find that Medusa task descriptions are two orders of magnitude smaller than standalone systems required to implement those crowd-sensing tasks, and the runtime has low overhead and is robust to dynamics and resource attacks.
Public Review uploaded by LandonCox:
This public review was prepared by Landon Cox.
This paper describes Medusa, a framework for programmatically
composing functionality from a set of distributed platforms:
microtasking markets (e.g., Mechanical Turk), compute utilities (e.g.,
EC2), and smartphone-based sensing. Prior systems have provided
partial solutions by supporting applications that execute across
various combinations of these platforms, but Medusa's primary
contribution is to show that it is feasible to tie all of these
platforms together through a simple and expressive scripting language
and runtime. Furthermore, the simplicity of the Medusa scripting API
allows programmers to focus on the application logic without having to
worry about all of the subtle issues that the runtime has to manage,
including participant incentives, synchronization, fault tolerance,
and user privacy.
Reviewers' primary criticism of the paper was that, though a complete
and elegant system, the individual components within Medusa are
similar to work from other systems. Nonetheless, the reviewers felt
that the paper presented an compelling solution to a set of emerging
challenges, and that ultimately the value of the work was greater than
the sum of its constituent parts.